Cancer Dynamics Analysis Service

Cancers are complex dynamical systems involving multiple genomic alterations that disrupt the dynamic response of signaling networks. The heterogeneous nature of cancer, which results in highly variable drug response, is a major obstacle to developing effective cancer therapy. Researchers must decipher the state-space attractor dynamics of gene expression patterns and protein oscillations orchestrated by cancer stemness networks. Most of the current tools rely on statistical correlation methods. A toolbox of complex systems approaches has been used for reconstructing cancer networks, interpreting causal relationships in their time-series gene expression patterns, and assisting clinical decision-making in computational oncology.

Algorithm or Technique
Lyapunov exponents (λL)	Network science
Frequency spectra	Convergent cross mapping (CCM)
Fractal dimension	Entropy
Master equation	Waddington landscape reconstruction
Boolean networks	Deep learning neural networks
Reaction-diffusion equations	Recurrent neural networks (RNNs)
Computational simulations	Kolmogorov complexity, K(s)

Previous studies of cancer therapeutic response mostly focus on static analysis of genome-wide alterations, thus they are unable to unravel the dynamic, network-specific origin of variation. Alternative computational methods have been developed to analyze large genomic data sets based on cellular network topology, which consists of information of collective interactions between multiple components, such as genes and proteins, in an integrated manner. Compared to genomics analysis based on individual genomic alteration, the network topology-based approach is proven more effective to predict drug response (i.e., phenotype) from the genotypes, as well as classify and cluster cancer subtypes.

Key Steps of Computational Approach

Select functional genomic alterations from a large number of molecular changes reported by the cancer genomics database.
Construct cancer cell-specific network models by mapping the functional genomic alterations of distinct cancer cell lines into the interaction network.
Stratify cancer cells based on the network response profile to perturbations that can change the network dynamics.

CD ComputaBio has developed a network dynamics-based approach to integrate cancer genomics with dynamics of biological network for drug response prediction and design of drug combinations. The results can reveal network-specific drug targets that maximize signaling network-mediated cell response, providing a basis to design combinatorial therapeutic strategies for distinct cancer genomic subtypes. Contact us now for more service details.

References

Abicumaran Uthamacumaran. A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks. Patterns. 2021.
Choi, M., Shi, J., Zhu, Y. et al. Network dynamics-based cancer panel stratification for systemic prediction of anticancer drug response. Nat Commun 8, 1940 (2017).

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